Bangladeshi Native Vehicle Classification Based on Transfer Learning with Deep Convolutional Neural Network

Vehicle type classification plays an essential role in developing an intelligent transportation system (ITS). Based on the modern accomplishments of deep learning (DL) on image classification, we proposed a model based on transfer learning, incorporating data augmentation, for the recognition and cl...

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Autores principales:	Md Mahibul Hasan, Zhijie Wang, Muhammad Ather Iqbal Hussain, Kaniz Fatima
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	native vehicle type classification Deshi-BD vehicle dataset deep learning transfer learning ResNet-50 Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/ddc17f8e630043b38ea52c5be6620884
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spelling	oai:doaj.org-article:ddc17f8e630043b38ea52c5be66208842021-11-25T18:57:20ZBangladeshi Native Vehicle Classification Based on Transfer Learning with Deep Convolutional Neural Network10.3390/s212275451424-8220https://doaj.org/article/ddc17f8e630043b38ea52c5be66208842021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7545https://doaj.org/toc/1424-8220Vehicle type classification plays an essential role in developing an intelligent transportation system (ITS). Based on the modern accomplishments of deep learning (DL) on image classification, we proposed a model based on transfer learning, incorporating data augmentation, for the recognition and classification of Bangladeshi native vehicle types. An extensive dataset of Bangladeshi native vehicles, encompassing 10,440 images, was developed. Here, the images are categorized into 13 common vehicle classes in Bangladesh. The method utilized was a residual network (ResNet-50)-based model, with extra classification blocks added to improve performance. Here, vehicle type features were automatically extracted and categorized. While conducting the analysis, a variety of metrics was used for the evaluation, including accuracy, precision, recall, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>F</mi><mn>1</mn></msub><mtext> </mtext><mo>−</mo><mtext> </mtext><mi mathvariant="italic">Score</mi></mrow></semantics></math></inline-formula>. In spite of the changing physical properties of the vehicles, the proposed model achieved progressive accuracy. Our proposed method surpasses the existing baseline method as well as two pre-trained DL approaches, AlexNet and VGG-16. Based on result comparisons, we have seen that, in the classification of Bangladeshi native vehicle types, our suggested ResNet-50 pre-trained model achieves an accuracy of 98.00%.Md Mahibul HasanZhijie WangMuhammad Ather Iqbal HussainKaniz FatimaMDPI AGarticlenative vehicle type classificationDeshi-BD vehicle datasetdeep learningtransfer learningResNet-50Chemical technologyTP1-1185ENSensors, Vol 21, Iss 7545, p 7545 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	native vehicle type classification Deshi-BD vehicle dataset deep learning transfer learning ResNet-50 Chemical technology TP1-1185
spellingShingle	native vehicle type classification Deshi-BD vehicle dataset deep learning transfer learning ResNet-50 Chemical technology TP1-1185 Md Mahibul Hasan Zhijie Wang Muhammad Ather Iqbal Hussain Kaniz Fatima Bangladeshi Native Vehicle Classification Based on Transfer Learning with Deep Convolutional Neural Network
description	Vehicle type classification plays an essential role in developing an intelligent transportation system (ITS). Based on the modern accomplishments of deep learning (DL) on image classification, we proposed a model based on transfer learning, incorporating data augmentation, for the recognition and classification of Bangladeshi native vehicle types. An extensive dataset of Bangladeshi native vehicles, encompassing 10,440 images, was developed. Here, the images are categorized into 13 common vehicle classes in Bangladesh. The method utilized was a residual network (ResNet-50)-based model, with extra classification blocks added to improve performance. Here, vehicle type features were automatically extracted and categorized. While conducting the analysis, a variety of metrics was used for the evaluation, including accuracy, precision, recall, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>F</mi><mn>1</mn></msub><mtext> </mtext><mo>−</mo><mtext> </mtext><mi mathvariant="italic">Score</mi></mrow></semantics></math></inline-formula>. In spite of the changing physical properties of the vehicles, the proposed model achieved progressive accuracy. Our proposed method surpasses the existing baseline method as well as two pre-trained DL approaches, AlexNet and VGG-16. Based on result comparisons, we have seen that, in the classification of Bangladeshi native vehicle types, our suggested ResNet-50 pre-trained model achieves an accuracy of 98.00%.
format	article
author	Md Mahibul Hasan Zhijie Wang Muhammad Ather Iqbal Hussain Kaniz Fatima
author_facet	Md Mahibul Hasan Zhijie Wang Muhammad Ather Iqbal Hussain Kaniz Fatima
author_sort	Md Mahibul Hasan
title	Bangladeshi Native Vehicle Classification Based on Transfer Learning with Deep Convolutional Neural Network
title_short	Bangladeshi Native Vehicle Classification Based on Transfer Learning with Deep Convolutional Neural Network
title_full	Bangladeshi Native Vehicle Classification Based on Transfer Learning with Deep Convolutional Neural Network
title_fullStr	Bangladeshi Native Vehicle Classification Based on Transfer Learning with Deep Convolutional Neural Network
title_full_unstemmed	Bangladeshi Native Vehicle Classification Based on Transfer Learning with Deep Convolutional Neural Network
title_sort	bangladeshi native vehicle classification based on transfer learning with deep convolutional neural network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ddc17f8e630043b38ea52c5be6620884
work_keys_str_mv	AT mdmahibulhasan bangladeshinativevehicleclassificationbasedontransferlearningwithdeepconvolutionalneuralnetwork AT zhijiewang bangladeshinativevehicleclassificationbasedontransferlearningwithdeepconvolutionalneuralnetwork AT muhammadatheriqbalhussain bangladeshinativevehicleclassificationbasedontransferlearningwithdeepconvolutionalneuralnetwork AT kanizfatima bangladeshinativevehicleclassificationbasedontransferlearningwithdeepconvolutionalneuralnetwork
_version_	1718410495443075072

Bangladeshi Native Vehicle Classification Based on Transfer Learning with Deep Convolutional Neural Network

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